Applying Learning Curves to Modeling Future Coal and Gas Power

Article pubs.acs.org/EF

Applying Learning Curves to Modeling Future Coal and Gas Power Generation Technologies Chris Ordowich,† John Chase,† Daniel Steele,‡ Ripudaman Malhotra,*,‡ Michiaki Harada,§ and Keiji Makino§ †

Center for Science, Technology, and Economic Development, SRI International, Arlington, Virginia 22209, United States Chemical Science and Technology Laboratory, SRI International, Menlo Park, California 94025, United States § Japan Coal Energy Center (JCoal), Tokyo, Japan ‡

ABSTRACT: Coal and natural gas have and will likely continue to be key components of the world energy supply for years to come. Currently, the most efficient commercial technologies for power production are supercritical pulverized coal combustion (SCPC) and natural gas combustion with combined cycle (NGCC). Emerging technologies for more efficient power generation from coal include ultra-super-critical pulverized coal (USCPC), advanced ultra-super-critical PC, integrated gasification combined cycle (IGCC), integrated gasification fuel cell combined cycle (IGFC), and direct carbon fuel cell. They each have different capital and operating costs leading to different levelized cost of electricity (LCOE). To forecast each of these competing technologies under various scenarios of electricity demand, fuel cost, and research investment, we created a Power Technology Futures Model (PTFM) based on “learning curves” methodology. Technology learning curves are a powerful tool for forecasting anticipated performance improvements due to a broad range of technical improvements without specifying the parameters of every possible improvement. The model can help planners and policy makers explore, visualize, and communicate how research and development (R&D) investments in certain technologies affect the mix of technologies deployed in the future. We utilized the Analytica modeling package and included detailed economic calculations to estimate the levelized costs for several types of coal and natural gas power plants with and without carbon capture technologies. Future improvements in plant efficiency and reductions in capital and operating and mantainence (O&M) costs were modeled using technology learning curves that were established by a detailed analysis of historic performance data. We used published estimates of future demand and fuel costs where available, but the model allows the user to easily input other numbers as tables or equations. Adoption of carbon capture was modeled in a variety of ways including being driven by a carbon cap or a carbon tax. The results of the model depict the difficulty of meeting a 50% reduction in annual CO2 production by 2050, even with significant R&D investments, ambitious CO2 pricing, and decreased demand for energy from coal and natural gas.

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INTRODUCTION Coal and natural gas will almost certainly continue to be critical components of the world electricity supply for years to come. Currently, more than half of the global electricity demands are met using these fossil fuels, with coal and natural gas contributing 40% and 20% of global power generation, respectively.1 Coal is an attractive fossil fuel because energy extraction is significantly less expensive than it is from oil and natural gas. According to a 2007 report from the Massachusetts Institute of Technology (MIT), The Future of Coal,2 usable energy can be extracted from coal at a cost of between $1 and $2 (USD) per MMBtu, compared to between $6 and $12 (USD) per MMBtu for oil and natural gas. The U.S. Energy Information Administration (EIA) estimates that the global consumption of coal will increase 74% between 2004 and 2030.3 While the use of other power-generation sources, including renewable resources, is expected to increase, the widespread use of coal for power generation will likely continue for many years to come, given its cost competitiveness and the existing infrastructure that represents billions of dollars of capital investment. Natural gas is also likely to continue to be a key source of power, as it is able to quickly meet peak energy demands, and new techniques such as shale gas extraction have resulted in lower natural gas prices. Because of the important © 2011 American Chemical Society

role that coal and natural gas will likely continue to play in supporting global energy needs, achieving emerging environmental goals such as reduced CO2 emissions requires the development and widespread deployment of technologies to utilize these energy sources more efficiently. The challenges of increased coal and natural gas utilization are widely documented and concern anthropogenic greenhouse gas emissions and their relationship to global climate change. In addition, there are other pollutants that pose significant health and environmental risks such as sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4). Despite the evident environmental challenges, the global increase in the utilization of coal and natural gas is very likely. According to several energy consortia and environmental experts, the answer to the confounding problem of meeting energy needs while mitigating environmental degradation will be found in carbon capture technologies.4,5 CO2 capture and sequestration (CCS) represent a group of technologies focused on capturing CO2 emitted during fossil fuel combustion and Received: September 9, 2011 Revised: November 27, 2011 Published: November 28, 2011 753

dx.doi.org/10.1021/ef2013634 | Energy Fuels 2012, 26, 753−766

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Figure 1. Information flow through four modules of the model.

Currently, the most efficient commercial coal and gas technologies for power generation are supercritical pulverized coal combustion (SCPC) and natural gas combustion with combined cycle (NGCC). Emerging coal technologies for more efficient power generation include ultra-super-critical pulverized coal (USCPC), advanced ultra-super-critical pulverized coal (AUSCPC), integrated gasification combined cycle (IGCC), integrated gasification fuel cell combined cycle (IGFC), and direct carbon fuel cell (DCFC). Each of these technologies has different estimated capital and operating costs leading to different levelized costs of electricity. To forecast each of these competing technologies under various scenarios of electricity demand, fuel cost, and research investment, we created the Power Technology Futures Model (PTFM) to project the LCOE and efficiency of coal and natural gas power generation technologies through 2050. The model can help planners and policy makers explore, visualize, and communicate how research and development (R&D) investments in certain technologies affect the mix of technologies deployed in the future. The following sections describe the modeling framework and present some of the key findings derived from the model.

preventing it from being released into the atmosphere by storing it either deep underground or in alternative material phases. CCS techniques are applicable to various types of plants utilizing fossil fuels, such as natural gas combined cycle, pulverized coal, and integrated gasification combined cycle plants. These technologies can potentially dramatically reduce the amount of CO2 released in the atmosphere. However, CCS technologies are not sufficiently mature, and there are currently no production-scale coal plants that employ them. One of the principal challenges is the cost of utilizing these techniques at production scales. It is estimated that the cost of energy from a coal-fired power plant with CCS may be increased by 30 to 60%. Given the high costs and uncertainties associated with CCS technologies, determining the point at which investing in fossil fuel plants with CCS capabilities is fiscally sound is critically important. To explore the development of these new technologies, we created a forecast model to project the levelized cost and efficiency of competing coal and natural gas power generation technologies through 2050. We utilized the Analytica modeling package and included detailed economic calculations to estimate the levelized cost of electricity (LCOE) for several types of coal and natural gas power plants, with and without carbon capture technologies. Future improvements in plant efficiency and reductions in capital and operating and maintenance (O&M) costs were modeled using technology learning curves. Combined with demand and input cost forecasts, the learning curves were used to project the cost of electricity for each plant type over time. The adoption of various power generation technologies was modeled on a cost basis; carbon capture technologies were modeled in a variety of ways, including regulatory mechanisms, such as a CO2 cap, and economic mechanisms, such as a CO2 tax. While there is an extensive body of literature on the use of learning curves to forecast the cost and efficiency of environmental technologies, we think that this is the first time that learning curves have been combined with demand projections and an economic competition method for modeling the adoption of clean energy technologies.

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THE MODEL The PTFM is composed of four modules, as shown in Figure 1. First, the model includes a set of baseline inputs that define the initial conditions for each technology and the energy environment. Second, a list of alternatives specifies the technologies included in the model, ranging from existing technologies, such as supercritical pulverized coal, to nascent technologies, such as direct carbon fuel cells. For each year, the model takes the baseline state of each energy system, projects its efficiency and cost using learning curves and projected energy demand, and calculates the levelized cost for each technology. The levelized cost is then used to determine the amount of new capacity built for each technology in the next year. This process is repeated for each year through 2050, after which the model outputs efficiency, levelized cost, and various other cost and environmental measures. 754


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Figure 2. Examples of global-, plant-, and component-level inputs to the model.

Baseline Inputs. The PTFM inputs can be classified into three categories, as shown in Figure 2: global, plant, and component. Global inputs include variables such as energy demand projections, the price of fuels and CO2, and various financial variables. Plant level inputs include the plant efficiency, efficiency learning rates, the capacity factor, and types of CO2 capture technologies utilized. Lastly, plant components include capital and O&M costs for each component, such as the boiler and turbine, as well as learning rates for each component. All baseline model inputs were derived from authoritative sources. Baseline cost and efficiency estimates were primarily derived from data provided by the National Energy Technology Laboratory (NETL)6 and in consideration of other significant sources such as the Electric Power Research Institute (EPRI)7 and the MIT report.2 Our estimates for SCPC are shown in Table 1. An aggregate list of the plant-level inputs used in the analyses contained in this paper is included in Appendix A.

The estimates are consistent with energy demand projections in the MIT report.2 While some might argue that renewable energy sources will offset some of this growth beyond the EIA/ IEA projections (which took renewable resources into account), the steady rise of global energy demand driven by increased wealth in Brazil, Russia, India, China, and other developing countries may offset this trend. Baseline financial inputs such as the plant life, depreciation period, cost of debt and equity, and capital cost escalation rates are summarized in Appendix B. Learning Curves. The key driver of technology adoption in the PTFM is learning curves. Engineering models based on the detailed deconstruction of specific processes are well suited for modeling the efficiency and cost of current plant designs; however, engineering models are not well suited for modeling long-term future technological advancement, which involves significant uncertainty. For example, it is impossible to predict specific parameters of individual performance enhancements such as increases in boiler operating temperature or pressure. Technology learning curves provide a powerful tool for forecasting anticipated performance improvements due to a broad range of technical improvements without specifying the parameters of every possible improvement. Because of the complexity of power-generation systems and the number of possible advances or innovations through 2050, we chose to use learning curves to model changes in the efficiency and cost of power-generating technologies. Learning curves have been used to project increases in production efficiencies since the 1930s. One of the first uses of learning curves was in studies of cost-quantity relationships in aircraft production.9,10 The empirical relationship between efficiency/productivity gains and quantity was formalized in the 1960s by the Nobel Prize winning economist Kenneth Arrow.11 Currently, learning curves are a well-established methodology in many industries ranging from manufacturing12 to defense production. The Air Force Cost Analysis Handbook calls learning curves “an essential tool of Air Force cost estimating”.13 In the past decade, learning curves have also become increasingly popular in studies on energy technology development.8,14−18 This literature has extended the use of learning curves from cost−quantity to efficiency−quantity relationships.19,20 The most commonly used experience curve is the single-factor-experience curve, which relates cost or efficiency to cumulative output. This type of curve is defined by the equation below, where yi is cost or efficiency in time period i, xi is the cumulative production through period i,

Table 1. Supercritical Pulverized Coal Baseline Data (2006 Dollars)

efficiency (%) total cost ($/kW) boiler tail gas cleanup steam turbine CO2 capture CO2 compression O&M cost ($/MWH) fuel cost ($/MWH) CO2 emissions (kg/MMBTU)

no carbon capture

amine capture

oxy fuel

39.1 1575 797 358 420 0 0 8.3 15.7 92.0

27.2 2870 902 413 413 1027 116 14.0 22.6 9.2

28.4 2549 801 366 366 912 103 12.7 21.6 9.2

Energy demand projections were derived from estimates in the EIA’s Annual Energy Outlook3 and the International Energy Agency’s (IEA’s) World Energy Outlook and Technology Perspectives Baseline Scenario.8 These estimates are equivalent to a 2.5% compound annual growth rate in the demand for coal energy and a 2.2% compound annual growth rate in the demand for natural gas energy over the next 40 years. The IEA projects that the demand for coal energy will nearly triple by 2050, increasing from 9400 TWh in 2010 to 24 000 TWh. In addition, extending EIA 2030 projections through 2050 results in a doubling in the demand for natural gas energy from around 4000 TWh to 10 000 TWh, as shown in Figure 3. 755


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Figure 3. Projected global demand for coal and natural gas power, from 2008 to 2050.

Figure 4. Estimating of NGCC efficiency learning curve parameters.

sources.8,19,21−24 Our estimate of the NGCC thermal efficiency rate is 5.3%, implying that thermal efficiency increases by 5.3% for every doubling of world cumulative installed capacity. IEA (2006) estimated a 3.5% learning rate for thermal efficiency in pulverized coal plants between 1920 and 1985.19 The lower learning rate for pulverized coal in ref 19 is likely due to the focus on subcritical boilers. If the data had been extended to include supercritical boilers, the learning rate would likely be closer to that which we observe for NGCC plants. Table 2 shows examples of cost learning rates used in the PTFM. Capital and O&M cost learning rates for established technologies were primarily derived from ref 19. For newer technologies, such as IGFC, a combination of estimates from analogous technologies and expert technological judgment were used. The baseline installed capacity is the initial condition of the model derived for each technology from a combination of energy databases and reports.19,25 The learning minimum and

and a and b are constants that are estimated from historical data.

Single Factor Experience Curve: γi = axi−b There are four key parameters for each learning curve: the learning rate; the baseline installed capacity; the installed capacity learning minimum; and the installed capacity learning maximum. We conducted an extensive literature review to develop appropriate estimates for each of these parameters. The learning rate is defined as the percentage change in the variable of interest (efficiency or cost) caused by a doubling of installed capacity. Learning rates typically range from 0 to 30%. Learning rates can be estimated from historical data. For example, Figure 4 shows how data on improvements in efficiency at NGCC power plant installations from 1971 through 2009 were used to estimate a learning rate for NGCC thermal efficiency. Learning rates are calculated by fitting a power-law curve to a plot of efficiency and cumulative installed capacity. Historical NGCC efficiency estimates were derived from a variety of authoritative 756


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of electricity of each plant in that year. The basic premise of the model is that, given a certain demand for electricity, the technology that can deliver power with the lowest LCOE will be built first. However, there is a limit to how fast a given technology can be adopted. If the demand cannot be completely satisfied by the first technology, the technology with the next lowest LCOE will then be built. The model also includes a user-specified amount of demonstration plant capacity added each year to simulate R&D investment in technologies that are not yet cost competitive. These demonstration plants push technologies down the learning curve, driving down cost and increasing efficiency, possibly making the plant technology cost competitive in future iterations. The PTFM cycles through this process year by year. During each iteration, it calculates the LCOE from each technology, which changes every year as a result of learning effects. The model then attempts to meet energy demand with the least expensive option within the constraints and builds up capacity from successively less economical options. Constraints are formalized by an annual installed capacity growth rate limit, which was estimated from historical growth data for pulverized coal and NGCC plants to be about 10%.8,19,21,22 This ensures that it is impossible for one technology to expand at an unrealistic pace. The graph in Figure 6 shows an example of new installed capacity for each plant type over the 2050 time horizon. We see constrained growth in the lowest cost technologies over time with some succumbing to others as learning drives further cost decreases. In Figure 6, new SCPC plants are built through 2028, as there is not enough of an industrial base to permit significant expansion of other plant types. As more and more USCPC, AUSCPC, IGCC, and IGFC plants are built, faster absolute expansion is possible as a result of a larger industrial base. By 2050, IGFC plants become the dominant technology in this example scenario. Levelized Cost of Electricity. As noted above, new plant capacity is determined on the basis of the levelized cost of electricity of a plant. The model calculates the levelized cost of electricity in terms of $/MWh as the sum of the levelized cost of fuel, capital, O&M, and carbon. As an example, the levelized cost of fuel is calculated in two stages. First, the real fuel cost per unit of power is calculated using the equation below, where

Table 2. Pulverized Coal, NGCC, and IGFC Plant Component Cost Learning Rates capital cost boiler AP controls steam turbine CO2 capture GTCC CO2 capture GTCC fuel cell ASU gasifier sulfur removal/recovery

O&M cost

Pulverized Coal 0.06 0.15 0.12 0.22 0.06 0.15 0.11 0.05 NGCC 0.10 0.06 0.11 0.22 IGFC 0.10 0.06 0.15 0.15 0.10 0.05 0.14 0.12 0.11 0.22

source ref ref ref ref

19 19 19 19

ref 19 ref 19 ref ref ref ref ref

19 25 19/expert judgment 19/expert judgment 19/expert judgment

maximum specify the installed capacity where learning begins and ends. The minimum is specified for each plant separately, while the maximum is specified for all plants. This is consistent with the observation that consistent improvements in cost and efficiency do not begin to appear until some period of time after initial pilot and demonstration plants are built and that learning disappears or is greatly diminished after some level of installed capacity. We can see the impact of this modeling assumption in Figure 5, which shows the efficiency gains for an IGCC plant without carbon capture. Efficiency gains for this plant do not occur until 2011 when there are 5 GW (learning minimum) of installed IGCC capacity worldwide, and learning stops around 2025 once 100 GW (learning maximum) of capacity is installed. Estimates for learning minimums and maxima were derived from ref 19. New Plant Capacity. The learning curve model posits that learning results from doing. Increases in installed capacity (i.e., more doing) naturally lead to improvements in plant efficiency and decreases in plant component costs from the resulting learning. In the PTFM, new installed capacity is assigned to each technology in each year on the basis of the levelized cost

Figure 5. Example of efficiency learning minimum and maximum. 757


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Figure 6. Example of restricted growth on new installed capacity.

Ft is the fuel cost and Efftp is the efficiency of plant p in year t.

coal consumed, and the amount of CO2 emitted annually. Examples of some of these outputs are used to illustrate the key model results discussed in the next section.

⎛ $Ft ⎞ ⎛ 1 MBTU ⎞ ⎛ 3413 BTU ⎞ ⎟ ⎟ ⎜ ⎟⎜ FCtp = ⎜ ⎝ MBTU ⎠ ⎝ 1000000 BTU ⎠ ⎝ KWh ⎠ ⎛ 1000 KWh ⎞ ⎛ 1 ⎞ ⎟ ⎟ ⎜ ×⎜ ⎝ 1 MWh ⎠ ⎜⎝ Eff tp ⎟⎠

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MODEL RESULTS The PTFM was designed to flexibly explore a wide range of future energy scenarios. In addition to modeling technology trajectories for strategic planning uses, it can also be used to examine a number of significant policy issues. In particular, the model allows us to explore the challenges of meeting future CO2 targets and to identify the key drivers of changes in emissions. The model also explicitly includes R&D as an exogenous variable, which allows us to explore the impact of varying R&D levels. Lastly, the model can also be used to illustrate the first-mover advantage, in which a technology adopted early can experience a significant head start over technologies adopted later. Challenge of Meeting Future CO2 Targets. Historically, coal energy generation has created about 40% of total CO2 emissions.2 As a baseline, the MIT Report, The Future of Coal, estimates that about 9 gigatonnes (GT) of CO2 was produced from coal energy generation in the year 2000, with that number expected to rise to 32 GT in 2050 if no other action is taken. With a CO2 tax driving carbon capture adoption, the MIT report projects that a low CO2 price path would reduce CO2 production to 15 GT per year in 2050, while a high-price path would reduce annual CO2 production to about 5 GT/year. We used the PTFM to estimate the path of annual CO2 emissions under these same CO2 price assumptions. The results are shown in Figure 7. With no CO2 tax, the model projects that CO2 emissions from natural gas and coal will increase from about 9 GT per year to almost 24 GT annually in 2050, as shown in the graph in Figure 8. In this scenario, very few plants are built with carbon capture technologies because there is no economic incentive to do so. The few plants that are built with carbon capture in this scenario are solely a result of the baseline R&D that we specified. Under the low CO2 price path, beginning at around $5 per tonne and increasing at a rate of about 2.3% annually to reach around $40 per tonne in 2050, emissions from natural gas and coal increase from about 9 GT to about

The real fuel cost is then used to calculate the levelized cost of fuel in the equation below, where r is the discount rate and L is the plant life.

LCFuel, p =

r ∑tL= 1 1−

FCtp

(1 + r )t L 1 1+r

( )

Similar calculations are performed for the levelized cost of capital, levelized O&M cost, and levelized cost of CO2. The levelized cost of capital takes into account the depreciation cost, cost of debt, equity and taxes, and the capacity factor for each plant. The model’s levelized cost of electricity calculations for the baseline year were validated against estimates in NETL (2007).26 Table 3 shows the results of this validation for the levelized cost of capital for SCPC, IGCC, and NGCC plants. Table 3. Levelized Cost of Capital Validation ($/MWh) PTFM

NETL

supercritical PC IGCC (GE) NGCC

Without CCS 34.7 45.3 12.2 With CCS

34.7 45.3 12.2

supercritical PC IGCC (GE) NGCC

67.4 59.7 27.6

67.5 59.7 27.5

Outputs. The primary output of the PTFM is plant cost and efficiency over time. Other key outputs include the cumulative installed capacity of each technology, the amount of 758


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Figure 7. Sensitivity of annual CO2 production to CO2 tax assumptions of MIT study.

Figure 8. Sensitivity of annual CO2 production to existing plant retirement rate.

16 GT annually by 2050, consistent with the MIT results. Under the high CO2 price pathbeginning at around $25 per tonne and increasing at a rate of about 1.5% annually to reach around $100 per tonne in 2050emissions from natural gas and coal similarly increase from around 9 GT to around 16 GT per year in 2050. Under both the high and low CO2 tax price paths, the model indicates nearly identical CO2 production. There are a number of reasons why, even with a high CO2 tax, it is difficult to reach 5 GT of CO2 emissions by 2050. First, without retrofitting old plants with CCS technologies or retiring plants early, only a limited number of new plants are needed each year to meet increased energy demand and to replace retiring old plants. The PTFM explicitly accounts for plant retirements. The model assumes that existing coal plants will be retired in order of efficiency, with the least efficient existing plants being retired first. For the baseline case, the efficiency distribution is based on the U.S. coal plant fleet as

documented in the NETL Coal Power Plant Database. This distribution was converted into 10 performance classes in the model, with 10% of installed capacity assigned to each class and an average efficiency ranging from 24% to 40% assigned to each class. The baseline assumption for the fraction of coal installed capacity retired each year was estimated to be 5%, implying that, in 2010, about 40 GW of coal installed capacity was retired and needed to be replaced. We found that increasing the rate of retirement in the model had very little long-term impact on CO2 emissions. Figure 8 shows annual CO2 production for various existing plant retirement rates assuming the MIT (2007) high CO2 price path. Extremely rapid plant retirements, as high as 15% to 50% per year, can result in a short-term dip in CO2 emissions, after which emissions grow at a rate similar to that seen under the much slower baseline retirement scenario. Over the long-term, retirement rates ranging from the 5% baseline to as high as 50% 759


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Figure 9. Sensitivity of annual CO2 production to constant CO2 tax assumptions.

We find that a CO2 tax effectively drives adoption of CCS by 2050 only if set at a high value in the very near future. Slowly increasing price trajectories such as the MIT (2007) high trajectory do not drive CCS adoption soon enough to impact annual emissions by 2050. We also find that while a high constant CO2 tax of $100/tonne instituted immediately could keep coal CO2 emissions from increasing significantly, any further increases beyond a $100/tonne CO2 tax provide little benefit in terms of CO2 reduction because of the limits on the adoption of new technologies. The peak in CO2 emissions in the constant tax cases is due to emissions from existing and new non-carbon capture plants built in early years. In later years, once all existing plants have been retired and carbon capture technologies have matured, CO2 emissions fall and level off toward 2050. Significant R&D investment is another path to CCS adoption and reduced CO2 emissions, specifically, in investments in demonstration/pilot plants. The PTFM baseline case assumes a constant annual R&D investment equivalent to about 0.25 GW of installed capacity per year for each technology. This investment level was based on an estimate of CCS pilot projects worldwide. Increasing R&D levels can have a significant impact on CO2 emissions by more quickly moving technologies along their learning curves decreasing the cost of newer, less CO2-intensive plants. Figure 10 shows the impact of increasing R&D investment from a constant 0.25 GW per year to 0.5, 1, 2, and 5 GW per year on CO2 emissions under a constant $100/tonne CO2 tax. Increased R&D leads to significantly reduced CO2 emissions by 2050. A doubling of R&D investment results in about a 0.5 GT reduction in annual CO2 emissions by 2050. In the most dramatic example, CO2 emissions in 2050 fall to near 6.5 GT when annual investment is increased to about 5 GW per year. Such an investment would only go on until the dominant technology, which is IGFC from 2020 onward in these cases, is at a commercial scale, usually after about 5−10 years. Such an investment profile is consistent with the low-carbon technology market development timeline in IEA (2010).8 As an example of R&D cost, under the 5 GW annual R&D investment scenario, we could assume that a demonstration plant could output 250 MW, about half that of an average commercial plant. Therefore, investing in all six

result in 2050 annual CO2 emissions that are not vastly different, at 16 GT and 14 GT, respectively. Significantly increasing existing plant retirements has little impact on CO2 production because even the high CO2 price trajectory chosen in ref 2 is too low to make adopting CCS technologies economically viable over the long-term. Under the high-price trajectory, ref 2 estimated that almost 60% of coal plants would include carbon capture technologies in 2050. Under the same CO2 price assumption, we find that only about 1−2% of coal plants would include carbon capture in 2050 if left to economic competition. Very few plants are built with carbon capture in this modeled scenario because NGCC plants without carbon capture have the lowest levelized cost of electricity over nearly the entire 40-year time period analyzed, despite the increasing penalty for producing carbon. Only near the end of this time horizon is the modeled investment in carbon capture R&D sufficient to drive down the cost of plants with carbon capture to make them economical. In contrast, the MIT high price scenario assumes “universal, simultaneous participation,” and notes that “the achievement in CO2 emissions abatement ... are sensitive to the development and public acceptance of CCS technology and the timely provision of incentives to its commercial application.”27 There are two factors that can increase the economic attractiveness of CCS adoption: higher CO2 taxes to increase the cost of conventional coal energy generation; or increased R&D to decrease the cost of CCS-equipped plants. We looked at both options to explore how much increases in each could reduce CO2 emissions. In the high CO2 tax trajectory scenario, MIT assumed that the CO2 tax would grow from about $25 to $100 per tonne from 2008 to 2050. We modeled a constant CO2 tax of $100 per tonne over this entire time period and as shown in Figure 9, we found that this action resulted in predicted 2050 CO2 emissions of about 9.5 GT, about equal to emissions today, and a carbon capture adoption rate of about 60%. After testing the sensitivity of a variety of CO2 price trajectories, we find that it is very difficult to achieve annual CO2 emissions of less than 9 GT, as shown in Figure 9, plotting the CO2 emissions from a constant $100, $150, and $300 per tonne CO2 tax through 2050. 760


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Figure 10. Sensitivity of annual CO2 production to annual R&D investment.

Figure 11. Sensitivity of annual CO2 production to coal and natural gas energy demand.

gas power by 25, 50, and 75% and eliminating all growth in the demand for coal energy, assuming a $100/tonne constant CO2 tax. Each 25% decrease in coal and natural gas energy demand results in a decrease in about 1 GT in CO2 production by 2050. Modeling increases in CO2 taxes, increases in R&D, and reductions in demand in isolation result in reductions of annual CO2 emissions of around 10−30% by 2050. However, no single change approaches the 50% cut, to about 5 GT annually, in emissions that the IPCC estimates is required to limit global warming to 2 °C by 2050.29 A 50% cut in emissions is also roughly consistent with the estimate in ref 2, using the high CO2 price trajectory and the IEA’s BLUE map scenario in Energy Technology Perspectives (2010).8 We looked at combinations of variables that would lead to a halving of fossil fuel CO2 emissions by 2050. First, we began with the MIT high price trajectory because such a phased CO2 tax is likely representative of what would be politically feasible. As displayed in Figure 12, this results in an increase in 2050

technologies for 10 years at an average price per plant of $5 billion (double the estimated baseline capital costs for these plants) results in a total expenditure over 10 years of about $6 trillion dollars. This compares to $32.8 trillion over 40 years (2010−2050) in power-sector investment estimated by the IEA for their BLUE map scenario, which aims to cut power sector CO2 emissions by 50% by 2050.28 While possible, it is difficult to develop realistic scenarios in which annual CO2 emissions from coal and natural gas could be reduced by 50% by 2050. One of the most significant factors in future energy-related CO2 emissions is the total amount of electricity generated by coal and natural gas plants. This drives the number of new plants that need to be built. The model assumes a constant 2−3% growth in coal and natural gas energy demand. However, significantly increased adoption of renewable and nuclear energy could reduce the demand for new coal and gas capacity, reducing CO2 emissions. Figure 11 shows the impact of decreasing the future growth rate in demand for coal and natural 761


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Figure 12. Sensitivity of annual CO2 production to combinations of inputs.

Technology Trajectories. Thus far, we have looked at the impact of numerous variables on achieving 2050 CO2 emissions targets, with little focus on the underlying economics and technology choices. All of the results discussed above are determined by technology choices driven by economics. Specifically, technologies gain a first-mover advantage when they are adopted early, experiencing a significant head start over technologies adopted later, driving cost down and increasing efficiency. This phenomenon is observed in the PTFM as a result of the use of learning curves, as illustrated in Figure 13. Figure 13 shows the projected cumulative installed capacity and levelized cost of electricity of the alternative fossil fuel generation technologies assuming: (a) no CO2 tax; (b) a CO2 tax following the MIT (2007) high-trajectory; (c) significantly increased R&D investment; and (d) a high constant CO2 tax. Each of these scenarios is discussed in more detail below. (a) In the case with no CO2 tax, the incumbent technology, supercritical pulverized coal (SCPC) is the dominant technology and continues to be for the foreseeable future. In this scenario, the levelized cost of all technologies increases steadily through 2050 driven primarily by capital and fuel cost escalation. The levelized cost of natural gas combined cycle (NGCC) is initially close to that of SCPC but increases more rapidly as a result of a more rapid increase in the price of natural gas fuel relative to coal as projected in the EIA’s 2008 Annual Energy Outlook.31 Because it has the lowest LCOE through this time period, SCPC remains the dominant technology. In addition, with no CO2 tax, no economies of scale are achieved for CCS technologies, and CCS adoption is, therefore, nearly nonexistent. CO2 emissions from coal and natural gas are estimated to rise to almost 24 GT by 2050 under this scenario. (b) The technology trajectories change dramatically if we assume that there is a CO2 tax similar to the MIT (2007) high-price trajectory. In this case, NGCC is projected to be the dominant technology by 2020 and remains in that position through 2050. NGCC starts out as the lowest cost technology as a result of having almost 50% lower emissions with carbon capture technologies than do similar coal plants and remains the dominant technology through

annual emissions to 16 GT. We then assumed an extreme increase in R&D to 5 GW annually per technology. This resulted in a reduction of annual CO2 emissions to near today’s levels of around 9 GT annually in 2050. This is consistent with the IEA Technology Perspectives ACT map scenario.30 Next, we eliminated growth in demand from coal and natural gas, assuming the widespread adoption of renewables and nuclear power. This resulted in a further reduction in 2050 emissions to nearly 5 GT. The impact of these combined changes on the trajectory of CO2 output is shown Figure 12. We also looked at the impact of dramatic progress in developing efficient carbon capture technologies such as integrated coal gasification fuel cell (IGFC) plants. If IGFC plants were available at the beginning of the time period analyzed, it would be the most cost-effective carbon capture technology. Under the MIT high CO2 tax scenario, this new technology would have little impact on CO2 emissions because NGCC plants without carbon capture still remain the most cost-effective technology. However, if we assume a constant CO2 tax of $100/tonne, the rapid availability of IGFC technology results in a reduction in 2050 CO2 emissions to about 8 GT, about 1 GT less than under the $100/tonne tax baseline scenario described earlier. Thus, while the rapid development of a new technology could reduce future emissions, the ultimate impact is relatively small as a result of the fact that there would be limited capacity to build new plants in the short term. It is possible that a more carbon-efficient technology not currently modeled is rapidly developed in the future. However, this technology would face many of the same scale-up barriers facing the modeled technologies. Overall, we find that it is nearly impossible to achieve a 50% reduction in CO2 emissions from power generation without significantly increasing R&D investment, implementing a meaningful carbon tax, and significantly cutting demand for energy from coal and natural gas sources. These results are based on the assumptions inherent in the PTFM. Most importantly, the model assumes that learning curves drive efficiency increases and cost decreases and that technology adoption, specifically the adoption of carbon capture technologies, is based purely on economics. While this is a reasonable approximation of the energy market, there are other concerns, such as regulatory requirements, which could drive technology adoption and lead to outcomes different than those modeled here. 762


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Figure 13. Cumulative installed capacity/levelized cost of electricity scenarios by technology.

about 2035. While other coal-utilizing technologies become cost-effective at this point, limits on annual growth prevent

them from becoming dominant technologies until beyond the 2050 time horizon studied here. It should be noted at 763


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projected to continue to be the dominant coal power generation technology. With a phased CO2 tax and small investment in new coal technologies, NGCC is projected to be the dominant technology. Only early imposition of substantial CO2 taxes or significantly increased R&D in alternative coal technologies would lead to the widespread adoption of IGCC or IGFC technologies. In these cases, the timing and magnitude of the CO2 tax are the key variables. The rapid imposition of a large tax will tend to boost existing CO2 capture technologies, whose production can be ramped up quickly to achieve economies of scale such as IGCC. This leads to significant decreases in annual CO2 emissions by 2050 as a result of the long time period over which learning can occur. A slow, phased tax will tend to support more nascent CO 2 capture technologies, such as IGFC, that, with R&D support, may be more efficient in future years. However, with the phased price path, significant additional R&D investment is needed to be needed (scenario c) to achieve the same 2050 CO2 emissions goal as a more rapidly implemented CO2 tax (scenario d).

this point that fuel cost is currently treated as an endogenous variable. In reality, the rapid increase in NGCC capacity indicated in the figure would likely be mitigated to some extent by a corresponding increase in natural gas fuel cost due to increased demand. (c) The combination of the MIT (2007) high-trajectory CO2 tax with a 5-GW annual R&D investment significantly changes technology trajectories. The PTFM estimates that this R&D profile increases the competitiveness of IGFC, making it a significant source of new global installed capacity by 2025. The significant increase in R&D moves new technologies quickly down their learning curves and lowers the levelized cost of electricity produced from them. In the early years, this leads to decreases in the LCOE for the incumbent SCPC and NGCC technologies with carbon capture. Next, the model indicates that AUSCPC would become the most cost-effective technology in the near future, but further R&D investments quickly push IGFC down its learning curve, making it a more economical option. Initially, growth constraints restrain the adoption of IGFC technology, but with significant R&D investment, there is enough of an industrial base to rapidly expand IGFC generation, and IGFC becomes the most dominant power-generating technology from 2030 onward. It is worth noting that DCFC technologies show significant cost competitiveness by 2050 and would likely become an important technology past 2050. Under our baseline assumptions, R&D investments are made following a technology-neutral profile; in other words, all technologies that are not yet commercially viable receive the same level of investment. Additionally, the model cannot anticipate unknown technical hurdles or other considerations that will most certainly affect future investment decisions. The results in Figure 13 should not be interpreted as a prediction that IGFC would become the dominant power generation technology, but rather as an indicator that R&D investment can tip the balance in favor of an emerging technology, whether it is IGFC, AUSPC, or some other technology. This scenario results in 2050 annual CO2 emissions of about 9 GT. (d) Technology adoption can also be significantly impacted by the CO2 price path. The technology trajectory examples b and c assumed the MIT (2007) high CO2 price path. If we return to the assumption that annual the R&D investment is 0.25 GW per year but the change in the CO2 tax is a constant $100/tonne, we find that IGCC becomes the most dominant technology over the 2020− 2050 time horizon. This is due to the fact that, in the early years of the model, IGCC has the lowest levelized cost of the coal utilizing technologies with carbon capture. Only NGCC has a lower LCOE, but growth constraints prevent it alone from generating all new fossil-fuel-derived power. The need for IGCC in these early years leads to rapid installation and quick movement down the IGCC learning curve. By 2020, IGCC has a lower LCOE than NGCC and becomes the dominant technology. Only just before 2050 does another technology, IGFC, begin to challenge IGCC as the dominant technology. This scenario results in 2050 annual CO2 emissions of about 9 GT equal to that of scenario c, which included both the MIT (2007) hightrajectory tax and increased R&D investment. Technology trajectories in the PTFM are very sensitive to initial investments and costs. Overall, without any action, the adoption of CCS technologies will be slow, and SCPC is

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CONCLUSIONS The Power for Technology Futures Model was designed to help make strategic decisions for investments in power generation technologies. The modeling approach allows a dynamic view of the impact of various inputs moving beyond restrictive scenarios to look at the impact of multiple variables on efficiency, cost, CO2 emissions, and technology trajectories. The results presented in this paper are sensitive to the many assumptions made within the model. Significant effort was expended in estimating baseline input values for the model. Nonetheless, there is considerable uncertainty around many of these values. In particular, forecasts of energy demand and fuel prices are inherently uncertain, and in some cases they are endogenous components of the model. Capital cost estimates are also subject to significant uncertainty; this is true to an even greater extent for nascent technologies. All models are simplifications of reality, and the PTFM is no different. The goal of building the model was to provide a framework for its users to explore the impact of different assumptions on technology adoption. This paper has explored some of the key implications of clean coal technology adoption for the environment. We have seen that it is very difficult to achieve a 50% reduction in annual CO2 production by 2050, even with significant R&D investments, ambitious CO2 pricing, and decreased demand for energy from coal and natural gas. Nonetheless, we find that changes in each of these factors can significantly alter the coal and natural gas technology landscape over the next 40 years. We also saw that modeling the impact of carbon taxes using economic competition led to results that differed significantly from MIT (2007) under the high-CO2 tax scenario. This has important policy implications as the model results imply that significant other action in addition to imposing a high CO2 tax would be required to achieve significant CO2 reductions. All of these results are driven by two factors: (1) that technological learning occurs, which allows new technologies to become more economically competitive to incumbent technologies through R&D investment; and (2) that energy investors are forward looking and consider the lifetime cost of a plant when deciding where to invest. The PTFM assumes some certainty in many of its parameters. In reality, knowledge of energy demand, prices, and CO2 tax levels over a 30-year time horizon is shrouded in uncertainty. This uncertainty could lead to conservative investment in proven technologies such as SCPC or NGCC, even if the best estimate points to another more cost-effective technology. 764


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Table A.1 supercritical

net power (MW) efficiency (%) total cost ($/kW) O&M cost ($/MWH) fuel cost ($/MWH) CO2 emissions (kg/MMBTU)

ultra-super-critical

advanced ultra-super-critical

no capture

amine capture

oxy fuel

no capture

amine capture

oxy fuel

no capture

amine capture

oxy fuel

550 39.1 1575 8.3 15.7 92.0

550 27.2 2870 14.0 22.6 9.2

550 28.4 2549 12.7 21.6 9.2

550 43.3 1611 10.5 14.8 93.0

550 34.1 2860 17.6 20.6 9.3

550 35.5 2540 16.0 19.7 9.3

550 48.0 1370 7.4 10.7 93.0

550 40.5 2368 12.5 12.6 9.3

550 41.6 2102 11.3 12.0 9.3

Table A.2 NGCC net power (MW) efficiency (%) total cost ($/kW) O&M cost ($/MWH) fuel cost ($/MWH) CO2 emissions (kg/MMBTU)

IGCC no capture

capture

no capture

capture

no capture

capture

560 50.8 554 2.7 45.3 54

480 43.7 1172 4.8 52.7 5.4

640 38.0 1813 11.5 16.1 90

560 32.5 2390 14.3 19.0 9

640 49.0 1774 12.9 12.5 95

560 42.3 2135 13.9 14.5 0

100 64.5 2323 10.9 7.9 93

92 59.5 2750 16.8 8.6 0

Table B.1 plant life depreciation period discount rate tax rate capital charge depreciation rate percent debt financing cost of debt cost of equity cost of CO2 transport and sequestration ($/tonne) capital cost escalation rate

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30 years 15 years 3% 40% 8.4% 150% declining balance low risk = 50%, high risk = 45% low risk = 9%, high risk = 11% low risk = 12%, high risk = 12% $4.1 0.05%

AUTHOR INFORMATION

Corresponding Author *E-mail: [email protected]

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ACKNOWLEDGMENTS Sponsorship for this project by the Japan Coal Energy Center (JCoal) is gratefully acknowledged.

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REFERENCES

(1) Oil supplies only 5% of global power generation and is not examined in this paper. http://www.iea.org/textbase/nppdf/free/ 2011/key_world_energy_stats.pdf; Accessed Nov 10, 2011. (2) The Future of Coal; MIT: Cambridge, MA, 2007. (3) International Energy Outlook; Energy Information Administration (EIA): U.S., 2008; Available online: http://www.eia.doe.gov/oiaf/ieo/ coal.html; Accessed Nov 10, 2011. (4) See for example: Herzog, H. J. Golomb, D. Carbon Capture and Storage from Fossil Fuel Use. In Encyclopedia of Energy; Cleveland, C.J., Ed.; Elsevier Science Inc.: New York, 2004; pp 277−287. (5) Hazeldine, R. S. Carbon Capture and Storage: How Green Can Black Be? Science 2009, 325, 1647−1652. (6) Cost and Performance Baseline for Fossil Energy Plants: Volume 1: Bituminous Coal and Natural Gas to Electricity Final Report, DOE/ NETL-2007/1281, Revision 1, August 2007.

APPENDIX A: BASELINE PLANT INPUTS

Tables A.1 and A.2 show the plant-level inputs used in the analyses contained in this paper.

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DCFC

capture

The incorporation of uncertainty is one area where the current PTFM could be expanded. The software platform used to develop the model is easily able to incorporate such uncertainty if it can be parametrized. Other potential improvements to the model include an expansion to cover competing energy technologies not included in the current model such as nuclear, wind, and solar. The expansion in the use of renewable resources has led to greater availability of information that would allow the modeling of these technologies. Cost and efficiency information for nuclear plants is also readily available.30 Shale fuel sources are other alternatives to which the model could be extended. The increasing availability of shale gas could result in lower than anticipated natural gas prices, which would make NGCC an even more attractive technology but would do little to change baseline emissions, as NGCC is already a dominant technology under the baseline scenario. The model could also be expanded to address endogenous variables, such as fuel costs, that are affected by the relative demand in fuels from different sources. This would require estimating direct and cross-price elasticities of demand for different fuel types. Lastly, the existing model could be made more detailed through the modeling of CCS retrofitting, biomass cofiring, and shifting among coal fuel sources for the existing fleets. While all of these enhancements would improve estimates of energy technology trajectories, the existing model provides a rigorous approach that captures the key drivers of clean fossil fuel energy technology development. This allows the derivation of important insights into the impact of various energy policy and investment decisions.

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IGFC

no capture

APPENDIX B: BASELINE FINANCIAL AND OTHER COST FACTORS

Table B.1 summarizes baseline financial inputs used in this paper. 765


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(7) Program on Technology Innovation: Evaluation of Amine-Based, Post-Combustion CO2 Capture Plants; EPRI: Palo Alto, CA, 2005; Product no. 1011402. (8) (a) International Energy Agency, World Energy Outlook, http:// www.iea.org/index_info.asp?id=2153; Accessed Nov 10, 2011 and Nov 14, 2008; (b) Energy Technology Perspectives; International Energy Agency (IEA): Paris, France, 2008. (9) Wright, T. P. Factors Affecting the Cost of Airplanes. J. Aeronaut. Sci. 1936, 3, 122−128. (10) Asher, H. Cost-Quantity Relationships in the Airframe Industry, R-291; The RAND Corporation: Santa Monica, CA, 1956. (11) Arrow, K. The Economic Implications of Learning by Doing. The Review of Economic Studies 1962, 29 (3), 155−173. (12) Argote, L.; Epple, D. Learning Curves in Manufacturing. Science 1990, 247 (4959), 920−924. (13) Air Force Cost Analysis Handbook, Air Force Cost Analysis Agency: Washington, DC, March 2007. (14) McDonald, A.; Schrattenholzer, L. Learning Rates for Energy Technologies. Energy Policy 2001, 29, 255−261. (15) Riahia, K. E.; Rubin, M.; Taylor, L.; Schrattenholzer, D.; Hounshell. Technological Learning for Carbon Capture and Sequestration Technologies. Energy Economics 2004, 26, 539−564. (16) Papineau, M. An Economic Perspective on Experience Curves and Dynamic Economies in Renewable Energy Technologies. Energy Policy 2006, 34, 422−432. (17) Rubin, E.; Yeh, S.; Antes, M.; Berkenpas, M.; Davidson, J. Use of Experience Curves to Estimate the Future Cost of Power Plants with CO2 Capture. Int. J. Greenhouse Gas Control 2007, 188−197. (18) Soderholm, P.; Sundqvist, T. Empirical Challenges in the Use of Learning Curves for Assessing the Economic Prospects of Renewable Energy Technologies. Renewable Energy 2007, 32, 2559−2578. (19) IEA Greenhouse Gas R&D Program. Estimating the Future Trends in the Cost of CO 2 Capture Technologies; IEAGHG: Gloucestershire, U.K., 2006. (20) Yeh, S.; Rubin, E. S. A Centurial History of Technological Change and Learning Curves for Pulverized Coal-Fired Utility Boilers. Energy 2007, 32 (10), 1996−2005. (21) Chase, D. L. Combined-Cycle Development Evolution and Future, GE Reference Document GER-4206; GE Power Systems: Schenectady, NY, 2001. (22) Beér, J. M. High Efficiency Electric Power Generation: The Environmental Role. Prog. Energy Combust. Sci. 2007, 33 (2), 107− 134. (23) Bergek, A.; Tell, F.; Berggren, C.; Watson, J. Technological Capabilities and Late Shakeouts: Industrial Dynamics in the Advanced Gas Turbine Industry, 1987−2002. Industrial and Corporate Change 2008, 17 (2), 335−392. (24) Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal; EPRI, Palo Alto, CA; U.S. Department of Energy, Office of Fossil Energy, Germantown, MD; and U.S. Department of Energy/ NETL, Pittsburgh, PA, 2000; Product no. 1000316. (25) Schwoon, M. Learning-by-Doing, Learning Spillovers and the Diffusion of Fuel Cell Vehicles, Working Paper FNU-112. (26) Fossil Energy Power Plant Desk Reference, DOE/NETL-2007/ 1282; 2007. (27) Ref 2, p. 11. (28) Note that our $6 trillion estimate includes pilot plant investments only and does not include investment in basic and applied research and development. It is unclear whether the IEA estimate (ref 8) includes such costs. (29) Intergovernmental Panel on Climate Change, Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the IPCC; Pachauri, R. K., Reisinger, A., Eds.; IPCC: Geneva, Switzerland, 2007. (30) Severance, C. A. Business Risks and Costs of Nuclear Power. Energy Economy Online 2009; Available online: http:// energyeconomyonline.com/Nuclear_Costs.html.

(31) International Energy Outlook; Energy Information Administration (EIA): U.S., 2008; Available online: http://www.eia.gov/ forecasts/archive/ieo08/index.html.

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